Lecture 10: Normal Distribution and z Scores

These flashcards cover the key concepts related to Normal Distribution and z Scores as discussed in Lecture 10.

Normal Distribution

A special type of distribution that is bell-shaped, symmetric, and unimodal, where most data values are clustered near the mean.

Standard Normal Distribution

A normal distribution with a mean (µ) of 0 and a standard deviation (σ) of 1.

z Score

A transformed score that designates how many standard deviation units the corresponding raw score is above or below the mean.

Bell Curve

Another name for the normal distribution, representing its characteristic bell shape.

Asymptotic

Refers to the tails of the normal distribution that approach the x-axis but never touch it.

Unimodal

Refers to a distribution that has one mode or peak.

Symmetric

A property of normal distribution where the left and right sides are mirror images of each other.

Population Mean (µ)

The average value of a population, used in defining the normal distribution.

Standard Deviation (σ)

A measure of the amount of variation or dispersion in a set of values, which affects the shape of the normal distribution.

Theoretical Distribution

A distribution that can be modeled mathematically but may not be perfectly observed in real data.

Raw Score

The original data value before any transformation, such as calculating a z-score.

Parameters of Normal Distribution

The mean (µ) and standard deviation (σ) used to define a specific normal distribution.

Continuous Variable

A variable that can take any value within a range, as opposed to discrete variables, which have separate values.

Tails of Distribution

The parts of a distribution that extend away from the mean, particularly in normal distribution.